Shili Lin, PhD, Department of Statistics, The Ohio State University Addressing the Correlated Feature in Sequencing-Based DNA Methylation Data for Detection of Diﬀerentially Methylated Regions DNA methylation is an epigenetic change occurring in genomic CpG sequences that con-tributes to the regulation of gene transcription both in normal and malignant cells. In recent years, aided by fast parallel sequencing technology, a number of genome-wide platforms have been developed to provide high throughput DNA methylation data. They can be classiﬁed as bisulﬁte-based (BS-seq) or capture-based (Cap-seq), both producing a massive amount of data. Numerous sophisticated statistical methods have been developed to analyze both types of data, but they are mainly for detecting diﬀerentially methylated loci (DMLs), although diﬀerentially methylated regions (DMRs) are often of more relevance biologically. The most prominent feature that is often overlooked in DML detection methods is the correlation in methylation signals in neighboring CpG sites. In this talk, I will discuss several statistical methods for analyzing both BS-seq and Cap-seq data to detect DMRs between two groups. In particular, I will highlight methods that take correlations into consideration and provide conﬁdence bounds of DMRs.